Toward improving seasonal snowpack predictions in complex mountainous terrain with high-resolution regional climate simulations

Laurie Huning, University of California, Los Angeles

Usage Details

Snowmelt derived from mountainous regions supplies a significant portion of the global population with freshwater. General circulation models are often too coarse to capture the heterogeneities controlling the variability of snow accumulation/melt in complex terrain. This project uses a high-resolution regional climate model (RCM) to better resolve topography and investigate snow states/fluxes/processes (e.g. snow water equivalent (SWE) distribution, accumulation/melt rate, peak accumulation, etc.) over montane regions. It aims to identify deficiencies in RCM snowpack estimates by gaining insight into the spatiotemporal variability of subgrid-scale snow processes/states. Case studies over Sierra Nevada, California will be evaluated with a novel spatially-distributed SWE reanalysis dataset. Lessons learned from the simulations and reanalysis will inform the development and implementation of subgrid parameterizations (e.g. orography) to enhance snow predictions from climate models over montane systems. Blue Waters provides an efficient environment for running high-resolution multi-year simulations requiring high volumes of parallel communication and data input/output.